Snyder David, Goudarzi Alireza, Teuscher Christof
Portland State University, 1900 SW 4th Avenue, Portland, Oregon 97206, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Apr;87(4):042808. doi: 10.1103/PhysRevE.87.042808. Epub 2013 Apr 16.
This paper underscores the conjecture that intrinsic computation is maximal in systems at the "edge of chaos". We study the relationship between dynamics and computational capability in random Boolean networks (RBN) for reservoir computing (RC). RC is a computational paradigm in which a trained readout layer interprets the dynamics of an excitable component (called the reservoir) that is perturbed by external input. The reservoir is often implemented as a homogeneous recurrent neural network, but there has been little investigation into the properties of reservoirs that are discrete and heterogeneous. Random Boolean networks are generic and heterogeneous dynamical systems and here we use them as the reservoir. A RBN is typically a closed system; to use it as a reservoir we extend it with an input layer. As a consequence of perturbation, the RBN does not necessarily fall into an attractor. Computational capability in RC arises from a tradeoff between separability and fading memory of inputs. We find the balance of these properties predictive of classification power and optimal at critical connectivity. These results are relevant to the construction of devices which exploit the intrinsic dynamics of complex heterogeneous systems, such as biomolecular substrates.
内在计算在处于“混沌边缘”的系统中是最大的。我们研究了用于储层计算（RC）的随机布尔网络（RBN）中动力学与计算能力之间的关系。储层计算是一种计算范式，其中经过训练的读出层解释受外部输入扰动的可兴奋组件（称为储层）的动力学。储层通常被实现为均匀递归神经网络，但对于离散且异质的储层特性的研究很少。随机布尔网络是通用且异质的动力系统，在这里我们将它们用作储层。随机布尔网络通常是一个封闭系统；为了将其用作储层，我们用输入层对其进行扩展。由于扰动，随机布尔网络不一定会落入吸引子。储层计算中的计算能力源于输入的可分离性和记忆衰退之间的权衡。我们发现这些特性的平衡可预测分类能力，并且在临界连通性时达到最优。这些结果与利用复杂异质系统（如生物分子底物）的内在动力学的设备构建相关。
